Evidence map›Paper›PMID 20488578›Full record

ReviewTrends in ecology & evolution2010

Approximate Bayesian Computation (ABC) in practice.

Katalin Csilléry, Michael G B Blum, Oscar E Gaggiotti, Olivier François

Abstract readReview
PubMed Publisher
In one paragraph

Review in Trends in ecology & evolution, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 445 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
445citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

445 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. A Review of Network Models for HIV Spread.Journal of acquired immune deficiency syndromes (1999) · 2025
    Pooled it
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385 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Katalin CsilléryLaboratoire Techniques de l'Ingénierie Médicale et de la Complexité, Centre National de la Recherche Scientifique UMR5525, Université Joseph Fourier, 38706 La Tronche, France. kati.csillery@gmail.com
Michael G B Blum
Oscar E Gaggiotti
Olivier François

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the forces that influence natural variation within and among populations has been a major objective of evolutionary biologists for decades. Motivated by the growth in computational power and data complexity, modern approaches to this question make intensive use of simulation methods. Approximate Bayesian Computation (ABC) is one of these methods. Here we review the foundations of ABC, its recent algorithmic developments, and its applications in evolutionary biology and ecology. We argue that the use of ABC should incorporate all aspects of Bayesian data analysis: formulation, fitting, and improvement of a model. ABC can be a powerful tool to make inferences with complex models if these principles are carefully applied.

Indexed as

Bayes TheoremBiological EvolutionAfricaAlgorithmsAnimalsBiodiversityBiostatisticsDemographyDrosophila melanogasterModels, Genetic

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.